(Received 19 November 1999; accepted in revised form 27 September
2000)

ABSTRACT. The spatial and temporal relationships between belugas
(Delphinapterus leucas) and two characteristics of their
habitat--bathymetry and ice concentration--were examined. Observed
location-habitat correspondence histograms were compared to random
location-habitat histograms, using a Kolmogorov-Smirnoff (K-S)
statistical test. Results show that beluga distribution is bimodal with
respect to bathymetry, with a larger mode in shallow water and a smaller
mode in water approximately 500 m deep. They occur more often than
expected by chance in the 0/10 ice class and less often than expected in
the 10/10 ice class. Males and females associate differently with both
depth and ice concentration. Females associate with bathymetry very
differently in the fall than in the summer. There is a general tendency
for males in the eastern North American Arctic to be associated with
shallow water during the summer and deeper water (modes at 100 and 500
m) in the fall. Female locations are associated more often with the 0
/10 ice class and less often with the 10/10 class than expected by
chance. These trends were stronger in the western than in the eastern
portions of the Canadian Arctic.

The polar regions of the planet will likely experience an increase
in temperature in response to a [CO.sub.2] enhanced atmosphere (IPCC,
1996). It is thought that this increase will be due to a variety of
feedback mechanisms operating across the ocean-sea ice-atmosphere
interface. Of several mechanisms identified, the "sea
ice-albedo" feedback mechanism appears to be a significant
component of the system. This mechanism illustrates that as the extent
of sea ice is reduced (particularly in spring and fall), more energy is
transferred from the atmosphere to the ocean. This positive feedback
raises the regional atmospheric temperature, thereby producing a further
reduction in ice concentration (percent cover per unit area).

Recent evidence suggests that this response has in fact already
begun. Between 1978 and 1998, there was an annual average reduction of
about 34 600 [km.sup.2] in the extent of sea ice over the entire
Northern Hemisphere (Parkinson et al., 1999). This reduction is
spatially heterogeneous, with larger decreases in particular locations
(e.g., the Chukchi and Laptev Seas) and slight increases in extent in
other regions (e.g., Baffin Bay). Independent evidence confirms a
reduction in both the areal extent of the sea ice (Johannessen et al.,
1999) and its thickness (Rothrock et al., 1999). Strategies by which
belugas (Delphinapterus leucas) respond and adapt to climate change will
depend on how and why they select particular habitats.

Relationships between belugas and their habitat have traditionally
been investigated using visual observations from boats and aircraft
(e.g., Moore and DeMaster, 1998) and by marking and occasionally
recapturing individual whales. Each of these approaches is restricted
spatially and temporally. Both approaches can provide useful information
for population indexing, but they cannot provide an overview of the
animal's behaviour in different types of habitat. Satellite-linked
tracking and dive recording have allowed researchers to increase their
understanding of beluga movements and behaviour (Martin and Smith, 1990;
Richard et al., 1998; Heide-J0rgensen et al., 1998). Satellite telemetry
provides locations of individuals several times daily and makes it
possible to track their movements and quantify their use of specific
marine or estuarine habitats. Geographic Information Systems (GIS) can
then use these telemetry locations to characterize the spatial and
temporal relationships between belugas and the habitats they occupy.

In this paper, we examine the relationships between beluga
occurrence and two environmental parameters: water depth and sea ice
concentration. As a preface to this analysis, we examine the assumption
of a continuous distribution of whale locations in time, as required by
our statistical analysis ("assumption testing"). We then
address our primary objective by examining three interrelated research
questions:

2) Gender- and Region-Based Habitat Selection: Does the
relationship in (1) vary by gender and region?

3) Gender- and Season-Based Habitat Selection: Does the
relationship in (1) vary by gender and season?

To date, most beluga studies have taken place in estuarine or
coastal summer habitats. The results of broad-scale aerial surveys and
shore-based watches have been interpreted as indicating that belugas
prefer loose to heavy pack ice (Finley et al., 1982; McLaren and Davis,
1982). Heavy ice has been hypothesized to function as a barrier to
beluga movement across the central North American Arctic (Sergeant and
Brodie, 1975). We reconsider this hypothesis in the present study.

METHODS

Telemetry and habitat data were available from a large geographic
region in the North American Arctic. We separated these data into
eastern and western subsets (Fig. 1). Whales were tagged in multiple
years from three locations: the Mackenzie Estuary, the south coast of
Devon Island, and the north and east coasts of Somerset Island (Richard
et al., 2001a, b). In the west, tags were attached to 30 whales and
operated within the periods 10 July-3 October 1993, 4 July-21 October
1995, and 27 July-2 October 1997. In the east, tags were attached to 26
animals and operated within the periods 12 September-4 November 1995, 3
September-18 November 1996, and 14 July- 27 November 1996. From these
tagged animals, we obtained a total of 26046 whale locations. We
excluded low-accuracy locations, reducing the data to a subset of 19969
locations. This subset had the advantage of decreasing the within-sample
variability in positional accuracy.

Telemetry

The belugas were live-captured and equipped with satellite-linked
position transmitters ("tags"). Tags consisted of a housing,
sensors to record data, an antenna, lithium batteries, circuitry to
produce the signal itself, and a microprocessor. The microprocessor was
programmed to control the sensors, collect and compress data, and
trigger the transmitter at each surfacing. The tag started transmitting
data to the Argos satellite whenever the animal surfaced and exposed the
antenna. Transmissions were repeated every 40 seconds. The
transmitter's latitude and longitude were calculated from the
difference in signal frequency between repeated signals while the
satellite was passing overhead. Further details of the live-capture, tag
attachment, operation, and data processing are given in Richard et al.
(2001a, b).

The GIS Database

The GIS database was created using three sources of spatial
information: telemetry data, Digital Bathymetric Models (DBMs), and
weekly composite sea ice charts. The telemetry data were gathered using
the methods described above and were reduced to Microsoft
Excel-compatible spreadsheets. The spreadsheets were organized according
to either date or whale tag number. The DBMs were developed using TBase
or TerrainBase Global 5-minute Terrain Model and a bathymetric database
provided by the Geological Survey of Canada (GSC) and the Canadian
Hydrographic Service (CHS) (MacNab and Monahan, 1997). The latter data
set was determined to be more accurate than the TBase data set despite
the lack of depths in a few areas. Thus the two bathymetric data sets
were combined into a comprehensive DBM of the Arctic Ocean. Because of
the large volume of data, two separate models were created, one for the
eastern Arctic (east) and one for the western Arctic (west) (Fig. 1).
Weekly composite ice charts were acquired from the Canadi an Ice Service
(CIS) in both paper and digital form. Specific methods pertaining to
these sources of data are as follows:

The GEBCO digital bathymetric software was used to produce 100 m
contour interval bathymetric data for the study area. Shorelines
generated were based on the World Vector Shoreline 1:12 000 000 data
set. Each of these data sets was projected into a Lambert
conic-conformal reference system and rasterized. The GSC/CHS database
was also projected into the Lambert conic-conformal reference system and
overlaid onto the bathymetric map to fill gaps in the GEBCO dataset. As
a final step, the bathymetric model was "smoothed" using a
median 3 x 3 filter to reduce the presence of "spikes" or
"artifacts" that were sometimes generated by the interpolation
procedure.

Environment Canada ice charts were acquired in both analogue
(paper) and digital form. These data were reviewed for completeness, and
the paper maps were digitized and integrated with the existing digital
products. On-screen digitizing was used to segment the raster scans into
unique polygons of ice type and concentration. The "egg-codes"
present on the ice charts were used to determine the type and
concentration of each ice polygon. Examples of the bathymetry and sea
ice concentration images are provided (Fig. 1).

Our research questions required that we extract information on
depth and ice concentration for each whale as a function of location and
season. We facilitated this by producing a geometrically corrected
database of "overlays" of the variables of interest.
Polynomial models were computed to project the data into a common
geographic reference to a precision of better than 1 raster pixel (5
km). The number of whale locations on each map was determined by the
time frame of the ice charts (weekly). Each ice chart contained a
composite of ice concentration for a seven-day period. Data extraction
was carried out by digitally overlaying the individual whale location
maps onto the respective ice charts and DBM. Coincident ice and depth
variables were then extracted for each of the 19969 whale locations.

Spatial Analysis

In the present context, the term "location" means the
telemetry position and its associated spatial and temporal co-ordinate.
The habitat variables are bathymetry, expressed as depth classes, and
sea ice concentration, expressed as areal coverage in tenths. To create
a testable hypothesis, we needed to generate correspondence histograms
(i.e., whale location spatially coincident with a particular habitat
variable). Our test metric required the generation of a random
correspondence histogram. The premise here is that by generating a
number of random locations, we can examine the expected habitat
relationships under a "randomness" assumption. These random
locations were selected by enumerating the total number of available
locations (all areas of ocean but not land) and randomly selecting an
appropriate number from this set for each analysis. Our logical and
statistical assumptions required that three conditions be met. First,
the random set of locations generated had to be of size N, where N
equals the number of "real" whale locations derived from the
telemetry data set. Second, the habitat "space" had to be
exactly the same in both the real and random location cases. This meant
that land had to be excluded from the sample space when selecting the
random locations, and we needed to use the same spatial variables for
ice and depth. Finally, we also had to generate cumulative frequency
histograms of "real" locations with particular habitat
variables and the associated random correspondence histograms, using the
same habitat variables.

The resulting cumulative frequency histograms can be considered
continuous variables, since the location and habitat values could range
throughout the magnitude of the respective variables. The statistical
hypothesis to be tested was whether a histogram generated from the real
locations was statistically distinguishable from that which would arise
given a random distribution of whales within exactly the same available
habitat. The Kolmogorov-Smirnoff (K-S) statistic is a suitable metric
for contrasting two such cumulative frequency distributions (Sokal and
Rohlf, 1981). The premise of this analysis is that if there is a
statistical difference between the observed and the random locations
(for the same habitat), then we can examine the real location histogram
for insight regarding the habitat preferences of the telemetered whales.

Two types of bias can affect our analysis: system bias and
behavioural bias. System bias is created by the physical operations of
the telemetry system (tags and satellites). Behavioural bias is created
when there is an unequal probability of producing a telemetered position
because of the behaviour of the animal (e.g., spending more time diving;
remaining in areas of heavy ice, etc.). As a prerequisite to the
frequency analysis (described above), we examined these issues of bias
from the perspective of meeting the assumptions of our K-S statistic
(data continuity in space and time). To address system bias, we examined
the pattern of telemetry locations throughout a diurnal cycle for all
animals and then for animals separated according to region, season, and
gender. If the whale locations were uniformly distributed throughout a
diurnal cycle, we could be confident that the apparent habitat
relationships were unbiased. We were not concerned about the seasonal
continuity of tag locations since our analysis was b ased only upon the
co-occurrence of whale location and habitat (i.e., we needed a location
for an event to be included in the habitat analysis). When a tag stopped
transmitting, our frequencies were simply reduced in number, yet the
continuity assumption required for our K-S test was met (even though our
statistical precision was reduced with the smaller sample size).
Behavioural bias was a much more complicated issue since it was inherent
to the relationships under investigation. For the purpose of this paper,
we assumed that the behavioural bias was small and constant across
region, season, and gender. We do, however, refer to behavioural bias in
our interpretation of results.

The whale-habitat relationship indicates how the animals move in
relation to habitat features, but it does not reveal the geographic
distribution of the animals. To address geographic distribution, we
computed the frequency of locations within a 5 [km.sup.2] sampling
interval within the seasonal periods of 51 (summer: up to and including
September 18) and S2 (fall: after September 18) for both the east and
west subregions. The resulting GIS maps show the numerical frequency of
locations on a 5 [km.sup.2] sampling grid within each subregion. The
frequency data were spatially heterogeneous because animals may dwell
for an extended period within a 5 [km.sup.2] region, then travel rapidly
to another site. This behaviour would result in large dwell-time values
in one square, perhaps zero or very low values in the adjacent squares,
until eventually the animal increased its dwell-time within another
area. To establish a trend in these spatial relationships, we convolved
a circular Gaussian filter of radial dimension 10 with the original
dwell-time data. This technique creates a low-magnitude trend surface
showing the general dwell-time behaviour of the animals within each
region. We saved the trend surface as a grey-scale image and created a
contour plot of the dwell-time frequencies derived from the original
frequency data. Both of these data sets were geometrically corrected
onto a shoreline mask of the study sites, thereby showing the geographic
dwell-time behaviour of the whales for each region and season.

The contour plots and grey-scale trend surfaces in Figure 3 overlap
with the land in many locations because the radial Gaussian kernel
increases the spatial dimension of the trend surface in both X and Y.
Thus when animals occur in narrow channels, the technique smoothes this
relationship away from the centre point of high dwell-time, often
overlapping with adjacent land masses.

RESULTS AND DISCUSSION

Assumption Testing

Our analysis requires that the telemetry observations be
statistically continuous to meet the assumptions of the K-S test. To
examine this requirement, we plotted the frequency of locations for all
whales. This analysis revealed a small yet distinct diurnal bias in the
telemetry locations (Fig. 2). This periodicity is a direct result of the
orbital path of the Argos satellite. There are sinusoidal periods during
the orbital cycle when coverage of the telemetry area changes. We also
examined the frequency histograms for whales by region and season and
found the same periodic pattern (histograms not shown). This confirms
that there is a diurnal bias in the telemetry position locations but
that the distribution of locations is still continuous (no systematic
breaks), thus meeting the K-S test assumption. As with any systematic
bias, so long as the whale-habitat relationship does not follow the
periodicity bias exactly, we can consider our statistical relationship
to be an unbiased reflection of the true whale-habitat relationship.

To provide a spatial context for our statistical analysis, we
produced the frequency of location information (dwell-time) relative to
the geography of the west and east subregions. Results show that the
animals in the east were located most often (highest dwell-times) in
Peel Sound during the summer season. A second mode of high dwell-times
was associated with the east coast of Devon Island, and a distinct
travel corridor through Barrow Strait and Lancaster Sound connected
these high dwell-time locations. In the fall, the distribution of
dwell-times changed considerably. The highest dwell-times occurred
principally along the east coast of Devon Island, with a second mode
inside Jones Sound. The animals also occurred within the southern limits
of the North Water polynya and along the west coast of Greenland,
although the frequencies of dwell-times suggest that the animals were
travelling rapidly through these areas (low dwell-time values in Fig.
3).

In the west, the highest dwell-times were in the Mackenzie Estuary
and within a deep trench located within M'Clure Strait and Viscount
Melville Sound. An intermediate dwell-time mode was located offshore
along the edge of the Beaufort Sea pack ice, and smaller modes were
evident within Amundsen Gulf and in a travel corridor between the
Mackenzie Estuary and M'Clure Strait. The dwell-time data suggest
that the animals were much more diffusely distributed in the fall. Modes
of high dwell-time occurred again within the Mackenzie Estuary and
Amundsen Gulf and north along the Yukon Coast (near Cape Herschel).
There were also several intermediate modes located offshore near the
margins of the pack ice. The low dwell-time corridors suggest that
animals are using different travel corridors to reach locations of high
dwell-time (Fig. 3). Further descriptions of the movements that led to
these distributions of locations are given by Richard et al. (200 in,
b).

Habitat Selection

To address the first of our three research questions, we considered
all 19969 locations from both the east and west subregions. Frequency
distributions of whale locations versus bathymetry and a random sample
of locations relative to the same bathymetry produce two very different
histograms (Fig. 4). The histograms suggest that the animals are
associated with depth in a bimodal type of distribution, with large
modes in shallow water and in waters approximately 500 m deep (Fig. 4).
The cumulative frequency distribution (grey curve) suggests that there
is a strong difference between the two distributions, and results from
the K-S test strongly reject the null hypothesis that they arise from
the same parent distribution (p = 0.000). We conclude that the animals
are distributed relative to bathymetry in a much different way than
would be expected by chance. Although no causal relationship can be
inferred from this result, it would appear that belugas prefer
particular bathymetry classes. Belugas have long been kn own to
congregate in estuaries in the summer (Sergeant and Brodie, 1975),
apparently to enhance their skin molt (St. Aubin et al., 1990). In the
Beaufort Sea and Baffin Bay regions, they also seek deep areas (300-600
m) in the summer, particularly in Viscount Melville Sound, Amundsen
Gulf, and Peel Sound (Smith and Martin, 1994; Richard et al., 2001 a,
b). Other deep areas, such as Jones Sound and Lady Ann Strait in N.W.
Baffin Bay (400-800 m), are also visited in the fall (Richard et al.,
1998). Dives in those areas frequently reach the seabed, where the
whales are presumed to feed (Martin and Smith, 1990; Heide-Jorgensen et
al., 1998; Richard et al., 2001a, b).

A behavioural bias may have affected our results. If an animal
spends more time away from the surface in deep water than in shallow
water, there would be a tendency for the frequencies to decrease as a
function of depth. We speculate that this bias (if it exists) is very
small because the frequency histogram is decidedly bimodal. If a strong
bias existed, we would expect a more uniform reduction in frequencies
with depth, not a bimodal distribution (500 m and shallow water).

We also found a significant (p = 0.000) difference between the real
and the random locations relative to sea ice (Fig. 5). Direct
examination of the empirical probabilities of the real versus the random
distributions suggests that belugas occur more often than would be
expected by chance in the 0/10 ice class and less often in the 10/10
class. There is generally more agreement between the observed and random
locations for the intermediate ice concentrations (1/10 to 9/10). It is
interesting that, even though the frequency of whales located within the
10/10 class is lower than expected, the number of locations within this
heavy-ice concentration class is still large. In fact, the empirical
probabilities are larger in the 10/10 class than in the 0/10 class. It
is noteworthy that these belugas were using 10/10 ice concentration
areas to reach the central Arctic Archipelago even though heavy ice has
been hypothesized to act as a barrier to beluga movement across the
central North American Arctic (Sergeant and Bro die, 1975).

A behavioural bias may also occur within this analysis,
particularly in the heavier ice classes. This is because there would
tend to be fewer successful telemetry positions in the higher ice
concentrations. Given the large frequency in the 10/10 class (relative
to the 0/10 class), we consider this bias to be minimal.

Gender and Region-Based Habitat Selection

Our second research question concerns differences in these
whale-habitat relationships according to gender (Fig. 6). As expected,
the random female and random male distributions are statistically
indistinguishable (K-S, p value = 0.895). When we compare the real and
random distributions, we find for both females and males that the real
and random distributions are very different (K-S, p = 0.000 in each
case). Furthermore the real female and male distributions are
significantly different (K-S, p = 0.038) from each other. We speculate
that much of this difference may be due to the behaviour of females
accompanied by calves. Mother-calf pairs are believed to spend longer
periods in shallow water than other age or gender classes. The
histograms suggest that both males and females use most depths of 800 m
or less, and there is an indication of a bimodal association with deep
(-300-500) m) and shallow modes (Fig. 6). As stated earlier, this
bimodality is a result of the occurrence of belugas in estuarine
habitats a nd in deep channels in the archipelago and the deeper shelf
waters of the Beaufort Sea (Fig. 3). The female distribution displays a
wider mode around 350 m, and there appears to be more separation of the
modes for males than for females (i.e., there is a sharper decline in
frequency between the two modes for males). We speculate that this
difference is due to the segregation of males in the western Arctic in
summer. In 1993 and 1995, most males tagged in the Mackenzie Estuary
moved to Viscount Melville Sound, where they remained in a deep trench
500+ m deep (Richard et al., 2001a). Females in the western Arctic
frequently moved to a trough about 350+ m deep in Amundsen Gulf. Males
also spent some time in 1500 m depths off the continental shelf in the
Arctic basin (Fig. 6).

Cumulative frequency distributions of male and female locations
relative to ice concentration are significantly different than what
would be expected from an equivalent random distribution of locations
relative to ice concentration (p values in both cases are 0.000).
Females spent more time in light ice (0/10) and less in heavy ice
(10/10) than would be expected by chance. There is a tendency for
females to be associated with the intermediate ice classes more often
than would be expected by chance (larger individual frequencies in the
real versus the random histograms in Fig. 7). We speculate that the
higher-than-expected frequencies in the intermediate classes mean that
females are at times seeking that type of ice cover. The reason(s) could
be related to the presence of under-ice prey or to the shelter from wind
and waves offered by ice. We speculate that the lower-than-expected use
of areas with heavy ice is related to the presence of calves, which
makes females reluctant to risk ice entrapment. Also, the limited diving
abilities of calves might mean that they are unable to negotiate long
distances between breathing holes.

The data indicate that females and males associate with ice
concentration differently. The male and female histograms in Figure 7
show that the largest contribution to this statistical difference arises
from the fact that males are more strongly associated with the 0/10 ice
class.

The eastern and western animal locations are also distributed very
differently from their random location equivalents (both K-S, p = 0.000)
in relation to bathymetry (Fig. 8). Also, the animals in the east
associate with bathymetry very differently than the animals in the west
do (p = 0.000), even though the distributions of the random locations
relative to depth (east versus west) are not significantly different (p
= 0.103). This means that the distribution of the habitat (in this case,
depth) is not statistically distinguishable, but the whales in the two
regions select different depth strata. Belugas from the Mackenzie
Estuary use deep offshore areas on their way to M'Clure Strait
rather than using the shallower waters near Banks Island (cf. Figs. 3
and 8).

Consideration of the ice habitat variable indicates a situation
similar to that of bathymetry (Fig. 9). The distributions of real
locations in both the east and the west are significantly different from
their random counterparts (both K-S, p = 0.000). The difference between
the east random and the west random is also statistically significant (p
= 0.000); thus, the distribution of ice concentration differs between
the two regions. Eastern and western whales clearly associate with sea
ice in very different ways. For example, a higher proportion of whale
locations in the west is associated with the 0/10 ice class, and a lower
proportion is associated with the 10/10 class (Fig. 9). We speculate
that this distribution is a function of the fact that there is
proportionately less habitat with 0/10 ice coverage available in the
east than in the west. Moreover, the extent of ice-free areas (including
estuarine habitats) is smaller in the east.

In our next series of analyses, we examined how females associated
with bathymetry in the two regions (Fig. 10). Females in the west
strongly selected the shallow water class, whereas the eastern females
selected a much deeper (mean of about 400 m) and narrower range of
depths. The strong bimodal distribution in the west suggests that the
behaviour of whales there differs from that of whales in the east. The
short peak at about 350 m and the long tail to 2000 m are due to the
fact that western females moved back and forth between the Mackenzie
Estuary, the shelf break, and Amundsen Gulf, thereby covering many depth
strata. Females in the east spent most of the summer in the deep trench
(400+ m) of Franklin Strait (southern Peel Sound), where they ranged in
waters 5-450 m deep. Eastern females then moved in early fall to the
nearshore but deeper waters off southern and eastern Devon Island
(depths to 500 m), but we have few positions from this area.

The pattern for males is similar to that for females, and some
characteristics of the bathymetric relationship are more pronounced
(Fig. 11). For example, the bimodal distribution in the west is much
stronger for males. The sharp peak at about 500 in for western males is
due to the animals' spending extended periods in the deep trench of
Viscount Melville Sound. Eastern males moved across a greater variety of
depths than western males. There was still a strong selection for
shallow water, but also a more pronounced mode at about 500 m. The K-S
statistics confirm that the female and male distributions are
significantly different within each region (p = 0.000 in all cases).

Significant regional differences were also observed for each sex in
its relationship to ice concentration (Figs. 12 and 13). In the east,
there were significantly more whale locations in the 0/10 ice class and
fewer in the 10/10 class than would be expected by chance. In general, a
larger proportion of locations was also associated with 2/10 and 9/10
coverage (Figs. 12 and 13). Western females had more locations in the
0/10 class and fewer locations in the 10/10 class than expected by
chance. There were also large differences in the classes between 2/10
and 9/10 (Fig. 12).

Gender and Season-Based Habitat Selection

When season is considered, the number of combinations increases. In
this analysis, we consider only two seasonal periods: summer (S1: up to
and including Sept 18) and fall (S2: after Sept 18). To present
combinations of regions (E or W), sexes (M or F), and the observed
("real") versus random pairing, we required 16 histograms for
bathymetry (Fig. 14) and another 16 for sea ice concentration (Fig. 15).

Results for the bathymetry relationships (Fig. 14) show that in the
summer period, females in both the east (E, S1, F, Real) and the west
(W, S1, F, Real) distributions were significantly different from the
random location distributions. Also, females in the east associated with
bathymetry very differently in the fall (E, 52, F, Real). In general,
females preferred shallower water in the summer and deeper water in the
fall. Unfortunately, there were not enough female locations in the west
for the fall (W, S2, F, Real) to provide a meaningful comparison of the
cumulative frequency histograms.

Male locations in the east were significantly different from the
random distributions for both summer and fall (Fig. 14). During the
summer, males in the east tended to be associated with the shallow-water
classes, with a large, diffuse mode between 0 and 500 m. In the fall (E,
S2, M, Real), there was a deeper-water association, with modes at 100
and 500 m (Fig. 14). In the west, there were two distinct modes at about
500 m and 20 m (W, S1, M, Real). Again, there were not enough locations
for meaningful conclusions regarding the fall period in the west (W, S2,
M, Real).

Results for sea ice concentration showed several differences across
the various combinations of gender, region, and season (Fig. 15). More
female locations were associated with the low (0/10) and high (10/10)
ice concentration classes than expected by chance. In the east,
proportionally fewer locations were associated with the 0/10 ice
concentration and slightly more with the intermediate ice concentration
classes (E, S1, F, Real; Fig. 15). Male trends were similar. There were
more male locations than expected in the 0/10 class and fewer than
expected in the 10/10 class. In the west, there were also more locations
associated with the 0/10 class and fewer with the 10/10 class than
expected by chance. In the fall (S2), fewer male locations were
associated with the 0/10 ice class (E, S2, M, Real) in the east and with
the 10/10 class in the west (W, S2, M, Real) than in summer.

CONCLUSIONS

The objective of this paper was to examine the spatial and temporal
relationships of belugas with two characteristics of their habitat:
bathymetry and ice concentration. Considering two regions, two seasons,
and both sexes, whale distribution in relation to depth showed a bimodal
distribution, with the larger mode in shallow water and the smaller mode
in water approximately 500 m deep. There was also a large difference
between the real and the random distributions of whale locations in
relation to ice conditions. Belugas occurred more often than expected by
chance in the 0/10 ice class and less often in the 10/10 ice class.

Males and females associated differently with both depth and ice
concentration. The difference in association with ice concentration is
explained mainly by the fact that males associate with the 0/10 ice
class more consistently than females do.

Although belugas in the east and west have approximately the same
spatial distribution of depths available to them, they associate with
bathymetry differently. The reasons for this difference are currently
unknown, but they probably indicate a real difference in either the
ecological or behavioural characteristics of whales visiting these
regions. Spatial distribution of sea ice was different within the two
regions (because of atmospheric and oceanographic differences), and the
animals associated with sea ice differently within each region.

With regard to seasonal differences, females associated with
bathymetry very differently in the fall than in the summer. In general,
females preferred shallower water in the summer and deeper water in the
fall. Males in the east generally tended to be associated with the
shallow-water classes more during the summer than in fall. Female
locations in summer were associated more often with the 0/10 ice class
and less often with the 10/10 class than we would expect by chance.
These trends were stronger in the west than in the east.

Our "dwell-time" analysis showed that the animals in the
eastern Arctic occurred most often (highest dwell-times)in Peel Sound
during the summer and moved to the east coast of Devon Island in the
fall. In the western Arctic, the animals occurred most often in the
Mackenzie Estuary and within a deep trench in M'Clure Strait and
Viscount Melville Sound during the summer. In the fall, the whales
occurred in the Mackenzie Estuary and Amundsen Gulf and north along the
Yukon Coast. The dwell-time analysis showed that the spatial pattern of
animal locations varied significantly between the summer and fall in
both the east and the west.

The analyses presented here represent an initial attempt to
interpret a combined telemetry and habitat GIS database. The basic
methodological premise of our work so far has been that of
correspondence analysis (whale location and habitat variables coincide
in space and time). The next logical step is to examine spatial
association at scales larger than correspondence (i.e., distance
association). It would also be possible to look more deeply into animal
behaviour by using dwell-time as a proxy. For example, areas with low
dwell-times could be defined as travel corridors when whale headings are
estimated by examining movement direction relative to time. High
dwell-times can be related to behaviour by examining depth relationships
and diving behaviour within these regions. The GIS also provides a
logical framework for integration of other variables, such as
traditional ecological knowledge, aerial survey abundance estimates, and
more complex habitat variables such as ice type (e.g., first-year,
multiyear, new i ce), water temperature, and marine productivity
(chlorophyll).

Our results show that animals select particular classes of sea ice
concentration and water depth, presumably because both relate to factors
such as prey distribution, predation, weather, molting, and the rearing
of young. We see these results as a prerequisite to understanding why
belugas select particular habitats. Once their reasons are better
understood, we may be able to predict how belugas will respond to
changes in the biophysical conditions of the Arctic marine environment.

ACKNOWLEDGEMENTS

This work was supported by a Natural Sciences and Engineering
Research Council (NSERC) Grant to D. Barber; a service contract from the
Department of Fisheries and Oceans and the Fisheries Joint Management
Committee; and analysis support from the Centre for Earth Observation
Science, Department of Geography, University of Manitoba. We are
grateful to A.R. Martin and the Sea Mammal Research Unit and M.P.
Heide-Jorgensen of the Greenland Institute of Natural Resources for
their support in obtaining the location data and to Brad Sparling for
database development. Thanks also for a pre-review from R. Reeves and D.
St Aubin and blind reviews from two Arctic reviewers. We thank I.
Harouche and D. Fast (University of Manitoba) for drafting assistance.
Many others acknowledged in Richard et al. (2001a, b) made this work
possible through funding and assistance in field work and location data
processing.

RICHARD, P.R., HEIDE-J0RGENSEN, M.P., ORR, J.R., DIETZ, R., and
SMITH, T.G. 2001b. Summer and autumn movements and habitat use by
belugas in the Canadian High Arctic and adjacent areas. Arctic
54(3):207-222.

SERGEANT, D.E., and BRODIE, P.F. 1975. Identity, abundance, and
present status of populations of white whales, (Delphinapterus leucas)
in North America. Journal of the Fisheries Research Board of Canada 32:
1047-1054.

SMITH, T.G., and MARTIN, A.R. 1994. Distribution and movements of
belugas, Delphinapterus leucas, in the Canadian High Arctic. Canadian
Journal of Fisheries and Aquatic Science 51:1653-1663.